Solving Optimal Power Flow Problems with Improved Particle Swarm Optimization

The paper proposes an improved particle swarm optimization algorithm with preserving feasibility strategy and neighbor selection scheme for solving optimal power flow (OPF) problems. In the proposed algorithm, fitness function and constraints are handled separately. Fitness function is used for guiding particles flying and constraints for checking the feasibility of particles. When updating the memories, all the particles only keep feasible solutions in their memory. Constraint handling with preserving feasibility strategy overcomes the deficiency of penalty function, provides advantages of faster convergence and simpler manipulation, and greatly improves the performance of the algorithm. Neighbor selection scheme ensures that every particle adjusts its flying based on the information of its better performers in the neighborhood and enhances local exploitation ability of the algorithm. The proposed algorithm was tested on the IEEE 30 bus system. The test results show that the proposed algorithm is applicable and effective in the solution of OPF problems. Compared with evolutionary programming, the proposed algorithm has faster convergence speed and more stable convergence performance